Description

Introduction

Ground Reaction Force

Instrumented gait analysis has become a crucial assessment tool in clinics, hospitals and rehabilitation centers to understand various (pathological) human movement patterns. Such three dimensional gait analysis (3DGA) techniques use motion capturing techniques and the measurement of ground reaction forces (GRF) via force plates to estimate joint kinematics and kinetics. By applying inverse dynamic calculations to the kinematic and GRF data, kinetic quantities such as joint moments and powers can be estimated. Up to date, these measures build the “gold standard” in clinical gait analysis and allow for the assessment of a large amount of biomechanical gait parameters. The potential drawback of this approach is, that it is a very time consuming, requires highly trained staff to acquire valid and reliable results, and the acquisition costs for the measurement systems are very expensive.

The GRF represent the most commonly used biomechanical signals to analyze human gait, because the necessary equipment is affordable and the process of data collection is simple, therefore allowing for high patient throughput. In total the GRF comprises a vertical force component and shear forces in medio-lateral and anterior-posterior directions. In clinical practice out of these three signals several parameters are derived which are used for further analysis and decision making.

However, apart from the simplicity of GRF data capturing, physical therapists and clinicians are often faced with a vast amount of GRF data and the need to interpret these data correctly. Due to the absence of automated analysis methods, the inspection of the data is performed visually, which is time-consuming and leads to subjective assessments.

Automatic analysis methods bear the potential to provide objective measurements and assessments of the signals. Recently, different methods have been introduced for the automatic classification of gait patterns. Existing methods, however, usually focus only on one specific functional deficit and are not applicable to the broad range of deficits that occur in clinical praxis. Furthermore, existing techniques are developed on rather small (artificial) datasets that do not reflect the complexity of data captured in clinical praxis. A still unsolved problem is the automatic generation of a generally applicable model for normal behavior that takes different parameters as walking speed, gender, body height, etc. into account and helps to distinguish normal behavior from abnormal behavior.

In this project our research partner maintains a large dataset of GRF data that contains measurements of patients with different ages, weights, and a broad range of functional deficits as well as detailed clinical diagnoses for each patient. This exceptional large-scale dataset bears the potential to develop novel powerful analysis techniques for gait patterns that fulfill the strong requirements of therapists and clinicians and are thus applicable in clinical daily routine. The novel methods should in future support the expert in detecting pathological or abnormal behavior, in making medical diagnoses and in the assessment of rehabilitation and training progress.

Goals

From existing shortcomings in the literature we have identified the following scientific challenges that will be addressed in this project:

Build a robust model for normal gait behavior from large-scale data. We have access to a large database of heterogeneous GRF patterns which provides a solid basis for the generation of a robust normal behavior model that is invariant to different walking speed, ages, genders, and weights.

Design automatic classification algorithms that are able to robustly detect a large range of functional deficits from gait patterns. While related work focuses on a few deficits only, we investigate the simultaneous detection of a broad range of clinically relevant deficits.

Discover novel parameters for the characterization of functional deficits: Existing GRF parameters represent a broad set of measurements but do not represent all aspects of the signals, i.e. not all parts of the signals are used. We propose to adaptively learn feature representations for different classes of deficits. The resulting features may not only improve automated detection but also reveal insights about important signal characteristics for different deficits and thus foster knowledge discovery.

Robust similarity matching of heterogeneous gait patterns. A novel way to approach gait analysis and knowledge discovery is to send a given gait pattern as query to a database and to retrieve similar patterns together with their descriptions to support experts in making diagnoses. The major challenge in this context is to robustly measure similarity across heterogeneous patterns of hundreds of different patients with different age, gender, walking speed, and physical condition.